免疫遗传算法学习贝叶斯网等价类  

Learning Equivalence Classes of Bayesian Network with Immune Genetic Algorithm

作　　者：贾海洋[1] 刘大有[2] 陈娟[1] 关淞元[1] 刘欣[1] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林大学符号计算与知识工程教育部重点实验室,长春130012

出　　处：《吉林大学学报（理学版）》2009年第1期48-56,共9页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金重大项目基金(批准号:60496321);国家自然科学基金(批准号:6057307360603030605030166077309960703022);国家高技术研究发展计划863项目基金(批准号:2006AA10Z2452006AA10A309);教育部博士学科点专项科研基金(批准号:20070183057);吉林省科技发展计划重大项目基金(批准号:20020303);吉林省科技发展计划项目基金(批准号:20030523);欧盟项目TH/AsiaLink/010(批准号:111084)

摘　　要：针对遗传算法学习贝叶斯网存在的问题,提出一种基于骨架搜索的免疫遗传算法学习贝叶斯网等价类,该方法综合了基于约束和打分搜索的方法,可以在遗传过程中避免产生非法结构,并从骨架空间映射到等价类空间进行搜索.实验数据表明,免疫算子的使用可有效缩小搜索空间规模,加快收敛速度,提高执行效率.To the question of drawbacks in learning Bayesian network with genetic algorithm, an immune genetic algorithm was proposed and used to learn the structure of Bayesian network, which combines the constraint based approach with score-search based approach. The algorithm can avoid generating illegal structures ; by means of the property of Markov equivalence, the immune operators maps the search space from skeleton space to Markov equivalent class space. The experiment data show that the search space was decreased, compared with those of the genetic algorithm search in direct acyclic graph space, the convergence speed and the efficiency were improved.

关 键 词：贝叶斯网 结构学习 马尔科夫等价 免疫遗传算法 条件独立测试 

分 类 号：TP11[自动化与计算机技术—控制理论与控制工程]